From RPA to Agent, The Automation Market Will Witness a Ten-fold Growth
The global Robotic Process Automation (RPA) market initially witnessed a rapid surge in growth, but it has since entered a phase of deceleration in recent years. According to Gartner’s recent Critical Capabilities for RPA report, the growth rate plummeted from 63% in 2020 to 22% in 2022. This decline signals that the RPA market has transitioned from its initial exuberance to the early stages of maturity.
Nevertheless, the introduction of groundbreaking technologies like generative AI and large language models has the potential to rejuvenate the automation market. This could lead to a remarkable increase, potentially by a factor of 10 or even 100 times its current state. Furthermore, RPA vendors worldwide are actively enhancing their integration capabilities and competitiveness in anticipation of this transformative wave.
The Rise of Integrated Automation Platforms
With the rapid growth of the market and the continuous integration of new technologies, RPA product portfolios are expanding. They now include features like Intelligent Document Processing, Conversational AI, low code and no code tools, process mining, and decision automation. These parts are coming together to create a complete set of automation tools that can handle many different tasks. Experts from Gartner and Everest predict that RPA will keep growing and become a bigger part of automation in the future.
Companies are also adding other smart automation tools to RPA to get more value and do more work. According to numbers from Everest Group, using Intelligent Automation along with RPA has gone up from 38% in the first quarter of 2018 to 85% in the same period in 2022. Large enterprises are looking to make their digital tools work together more, so they will switch from using lots of separate tools to one integrated platform for Intelligent Automation.
From RPA to Agent: The ChatGPT Connection
In early 2023, ChatCPT, powered by large language models, became a big hit worldwide. What’s interesting is that almost every RPA company is now trying out, or planning to try out, generative AI like GPT models in their products. Some are even teaming up with large language model vendors through API.
Here’s why this matters: RPA is really good at doing tasks that follow clear rules and use structured data. On the other hand, large language models are really good at handling unstructured data and information, making decisions, and reasoning. When you put these two together, you get “agents” that can understand what people need through conversations, break down those needs into tasks, and actually do things for users. It’s a powerful combo that expands what RPA can do and makes users very happy.
It's increasingly important to make digital and human workers collaborate effectively. Digital workers powered by large language models can handle much more complex tasks than traditional bots. This has the potential to significantly boost productivity.
This is a long-term goal. For now, the focus should be on deeply integrating large language models into specific features. Suppliers who do this well are likely to win favour with customers and accumulate valuable experience. For example, at Laiye, we introduced a new LLM-powered feature called ‘Magic Hat’ as Laiye RPA's Copilot. This makes the tool more accessible to citizen developers, further democratizing its use.
Open Source Models Driving Ecosystem Innovation
As more open source models become available, the large language models landscape is evolving into a diverse portfolio. This includes general-purpose large language models as well as spcialized ones designed for specific domains like laws, medicine, or finance. Customized large language models tailored to a specific field perform exceptionally well and offer high precision.
This shift also gives a competitive edge to RPA vendors who embrace open approaches. They can choose from a range of models, providing flexibility to select the one that best suits their customers’ needs. This, in turn, leads to increased customer satisfaction and better overall performance. As a result, the competitive dynamics among RPA vendors are undergoing transformation, influenced by changing market trends, strategies, and priorities.
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